In past years, image capturing and processing technologies have significantly developed. At present, image capturing devices such as camera and video camera can already be integrated into various computing devices such as mobile phone, personal digital assistant (PDA), tablet computer, laptop computer and the like. In the current image capturing and processing technologies, one major challenge comes from the impact of the imaging case on image quality. For example, in a low-light environment or a foggy natural environment, the quality of the captured image would usually deteriorate significantly. For example, there might be scene blurring in the image.
Some solutions have been proposed to address this problem. For example, it has been proposed to capture a series of images using different parameters (e.g., exposure parameters, focal distance, etc.) each time when a user issues command of image capturing and to integrate these images to obtain a final image, thereby eliminating the negative impact caused by the low light. However, such method needs a higher computation cost, and not all image capturing devices allow control of parameters. More importantly, this solution cannot meet the requirement of real-time image enhancement. For example, although the quality of the final result may be enhanced, the quality of real-time image of the scene which is represented in the view-finder is not improved. Therefore, user experience during the image capturing process cannot be improved. Accordingly, such image enhancement solution is not suitable for real-time image/video enhancement.
Some other solutions solve this problem by virtue of computer software. For example, image quality in a low-light condition may be enhanced through improving the contrast of the image or video frame. Alternatively or additionally, a dedicated fog removal process or low light removal process may be performed so as to eliminate the foggy area or low-illumination area in the image based on an imaging model. However, images obtained by such method usually contain remarkable noises and are unstable. For example, the scale-like visual effects would probably be produced in the area subject to fog removal or low light removal, which introduces new noise during the image enhancement process. Such phenomenon may be referred to as “over-removal”, which causes the result of image enhancement unstable and unreliable and will affect the visual effect of the resulting image.
In view of the above, this field needs an image enhancement technology suitable for real-time image enhancement and meanwhile avoids over-removal.